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        If you use plots from MultiQC in a publication or presentation, please cite:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411
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        Tool Citations

        Please remember to cite the tools that you use in your analysis.

        To help with this, you can download publication details of the tools mentioned in this report:

        About MultiQC

        This report was generated using MultiQC, version 1.27

        You can see a YouTube video describing how to use MultiQC reports here: https://youtu.be/qPbIlO_KWN0

        For more information about MultiQC, including other videos and extensive documentation, please visit http://multiqc.info

        You can report bugs, suggest improvements and find the source code for MultiQC on GitHub: https://github.com/MultiQC/MultiQC

        MultiQC is published in Bioinformatics:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

        A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.

        This report has been generated by the nf-core/scrnaseq analysis pipeline. For information about how to interpret these results, please see the documentation.
        Report generated on 2025-05-04, 20:11 BST based on data in: /rds/projects/g/gendood-3dmucosa/BaseSpace/scripts/work/07/e591b43eeb7fe541d77366948f4f46

        General Statistics

        Showing 20/20 rows and 9/14 columns.
        Sample NameEstimated CellsReads In CellsAvg Reads/CellM ReadsValid BCQ30 BCQ30 readQ30 UMIDupsGCAvg lenMedian lenFailedSeqs
        P18_LPS-N
        3660
        66.0%
        11571
        42.3M
        96.8%
        95.1%
        93.5%
        95.1%
        P18_LPS-N_1
        6.6%
        50.0%
        28bp
        28bp
        0%
        21.2M
        P18_LPS-N_2
        51.0%
        47.0%
        90bp
        90bp
        9%
        21.2M
        P18_LPS-N_3
        6.5%
        50.0%
        28bp
        28bp
        0%
        21.1M
        P18_LPS-N_4
        50.9%
        47.0%
        90bp
        90bp
        9%
        21.1M
        P18_LPS-P
        5901
        86.7%
        8409
        49.6M
        97.0%
        95.0%
        93.3%
        95.0%
        P18_LPS-P_1
        6.9%
        50.0%
        28bp
        28bp
        0%
        24.9M
        P18_LPS-P_2
        51.7%
        47.0%
        90bp
        90bp
        9%
        24.9M
        P18_LPS-P_3
        6.9%
        50.0%
        28bp
        28bp
        0%
        24.8M
        P18_LPS-P_4
        51.6%
        47.0%
        90bp
        90bp
        9%
        24.8M
        P22_LPS-N
        3458
        75.8%
        10711
        37.0M
        97.0%
        94.9%
        93.1%
        94.9%
        P22_LPS-N_1
        5.8%
        50.0%
        28bp
        28bp
        0%
        18.6M
        P22_LPS-N_2
        49.5%
        47.0%
        90bp
        90bp
        0%
        18.6M
        P22_LPS-N_3
        5.9%
        50.0%
        28bp
        28bp
        0%
        18.4M
        P22_LPS-N_4
        49.6%
        47.0%
        90bp
        90bp
        0%
        18.4M
        P22_LPS-P
        2408
        61.5%
        14713
        35.4M
        96.8%
        94.6%
        93.0%
        94.6%
        P22_LPS-P_1
        5.7%
        50.0%
        28bp
        28bp
        0%
        17.8M
        P22_LPS-P_2
        50.0%
        47.0%
        90bp
        90bp
        0%
        17.8M
        P22_LPS-P_3
        5.5%
        50.0%
        28bp
        28bp
        0%
        17.6M
        P22_LPS-P_4
        50.0%
        47.0%
        90bp
        90bp
        0%
        17.6M

        Cell Ranger

        Analyzes single cell expression or VDJ data produced by 10X Genomics.URL: https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/what-is-cell-rangerDOI: 10.1038/ncomms14049

        Count - Warnings

        Warnings encountered during the analysis

        Showing 2/2 rows.
        Sample Namecellranger_reference_filtered_bcs_conf_mapped_barcoded_reads_cum_frac
        P18_LPS-NFAIL
        P22_LPS-PFAIL

        Count - Summary stats

        Summary QC metrics from Cell Ranger count

        Showing 4/4 rows and 9/20 columns.
        Sample NameM ReadsValid BCValid UMISaturationQ30 BCQ30 readQ30 UMIEstimated CellsReads In CellsAvg Reads/CellMedian UMI/CellMedian Genes/CellGenes DetectedReads MappedConfident ReadsConfident IntergenicConfident IntronicConfident ExonicConfident TranscriptomeReads Antisense
        P18_LPS-N
        42.3
        96.8%
        100.0%
        11.5%
        95.1%
        93.5%
        95.1%
        3660
        66.0%
        11571
        1807.0
        1072.0
        26419.0
        43.7%
        35.6%
        1.0%
        4.0%
        30.6%
        32.8%
        1.4%
        P18_LPS-P
        49.6
        97.0%
        100.0%
        12.3%
        95.0%
        93.3%
        95.0%
        5901
        86.7%
        8409
        2370.0
        1304.0
        29808.0
        58.4%
        50.2%
        1.6%
        7.6%
        41.0%
        45.7%
        2.2%
        P22_LPS-N
        37.0
        97.0%
        100.0%
        10.5%
        94.9%
        93.1%
        94.9%
        3458
        75.8%
        10711
        2472.0
        1399.0
        27301.0
        56.2%
        48.0%
        1.3%
        6.9%
        39.8%
        44.0%
        2.0%
        P22_LPS-P
        35.4
        96.8%
        100.0%
        9.8%
        94.6%
        93.0%
        94.6%
        2408
        61.5%
        14713
        1876.0
        1094.0
        25887.0
        43.7%
        35.2%
        1.1%
        3.9%
        30.2%
        32.2%
        1.3%

        Count - BC rank plot

        Barcode knee plot

        The plot shows the count of filtered UMIs mapped to each barcode. Barcodes are not determined to be cell-associated strictly based on their UMI count. Instead, they could be determined based on their expression profile, or removed via Protein Aggregate Detection and Filtering and/or High Occupancy GEM Filtering. Therefore, some regions of the graph contain both cell-associated and background-associated barcodes. The color of the graph in these regions is based on the local density of barcodes that are cell-associated. Hovering over the plot displays the total number and percentage of barcodes in that region called as cells along with the number of UMI counts for those barcodes and barcode rank, ordered in descending order of UMI counts.

        Created with MultiQC

        Count - Median genes

        Median gene counts per cell

        This plot shows the Median Genes per Cell as a function of downsampled sequencing depth in mean reads per cell, up to the observed sequencing depth. The slope of the curve near the endpoint can be interpreted as an upper bound to the benefit to be gained from increasing the sequencing depth beyond this point.

        Created with MultiQC

        Count - Saturation plot

        Sequencing saturation

        This plot shows the Sequencing Saturation metric as a function of downsampled sequencing depth (measured in mean reads per cell), up to the observed sequencing depth. Sequencing Saturation is a measure of the observed library complexity, and approaches 1.0 (100%) when all converted mRNA transcripts have been sequenced. The slope of the curve near the endpoint can be interpreted as an upper bound to the benefit to be gained from increasing the sequencing depth beyond this point. The dotted line is drawn at a value reasonably approximating the saturation point.

        Created with MultiQC

        FastQC

        Quality control tool for high throughput sequencing data.URL: http://www.bioinformatics.babraham.ac.uk/projects/fastqc

        Sequence Counts

        Sequence counts for each sample. Duplicate read counts are an estimate only.

        This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

        You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        Created with MultiQC

        Sequence Quality Histograms

        The mean quality value across each base position in the read.

        To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

        Taken from the FastQC help:

        The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

        Created with MultiQC

        Per Sequence Quality Scores

        The number of reads with average quality scores. Shows if a subset of reads has poor quality.

        From the FastQC help:

        The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

        Created with MultiQC

        Per Base Sequence Content

        The proportion of each base position for which each of the four normal DNA bases has been called.

        To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

        To see the data as a line plot, as in the original FastQC graph, click on a sample track.

        From the FastQC help:

        Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

        In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

        It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

        Click a sample row to see a line plot for that dataset.
        Rollover for sample name
        Position: -
        %T: -
        %C: -
        %A: -
        %G: -

        Per Sequence GC Content

        The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

        From the FastQC help:

        This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

        In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

        An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

        Created with MultiQC

        Per Base N Content

        The percentage of base calls at each position for which an N was called.

        From the FastQC help:

        If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

        It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

        Created with MultiQC

        Sequence Length Distribution

        All samples have sequences of a single length (90bp, 28bp) See the General Statistics Table.

        Sequence Duplication Levels

        The relative level of duplication found for every sequence.

        From the FastQC Help:

        In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (e.g. PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

        Created with MultiQC

        Overrepresented sequences by sample

        The total amount of overrepresented sequences found in each library.

        FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as overrepresented.

        Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

        From the FastQC Help:

        A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

        FastQC lists all the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

        16 samples had less than 1% of reads made up of overrepresented sequences

        Top overrepresented sequences

        Top overrepresented sequences across all samples. The table shows 20 most overrepresented sequences across all samples, ranked by the number of samples they occur in.

        Showing 0/0 rows.
        Overrepresented sequence

        Adapter Content

        The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

        Note that only samples with ≥ 0.1% adapter contamination are shown.

        There may be several lines per sample, as one is shown for each adapter detected in the file.

        From the FastQC Help:

        The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

        Created with MultiQC

        Status Checks

        Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

        Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

        In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

        Created with MultiQC

        Software Versions

        Software Versions lists versions of software tools extracted from file contents.

        GroupSoftwareVersion
        CELLRANGER_COUNTcellranger8.0.0
        CELLRANGER_MKGTFcellranger8.0.0
        CELLRANGER_MKREFcellranger8.0.0
        FastQCfastqc0.12.1
        MTX_TO_H5ADanndata0.10.8
        pandas2.2.2
        python3.12.5
        scanpy1.10.2
        WorkflowNextflow24.04.2
        nf-core/scrnaseqv4.0.0-ge0ddddb

        nf-core/scrnaseq Methods Description

        Suggested text and references to use when describing pipeline usage within the methods section of a publication.URL: https://github.com/nf-core/scrnaseq

        Methods

        Data was processed using nf-core/scrnaseq v4.0.0 (doi: 10.5281/zenodo.3568187) of the nf-core collection of workflows (Ewels et al., 2020), utilising reproducible software environments from the Bioconda (Grüning et al., 2018) and Biocontainers (da Veiga Leprevost et al., 2017) projects.

        The pipeline was executed with Nextflow v24.04.2 (Di Tommaso et al., 2017) with the following command:

        nextflow run nf-core/scrnaseq -r 4.0.0 --input ../VU40Tsamplesheet.csv --outdir ../LPS_VU40T_QC_and_counts --genome GRCh38 -profile bluebear --aligner cellranger --fasta ../../Refs/hg38.fa --gtf ../../Refs/hg38.ncbiRefSeq.gtf

        References

        • Di Tommaso, P., Chatzou, M., Floden, E. W., Barja, P. P., Palumbo, E., & Notredame, C. (2017). Nextflow enables reproducible computational workflows. Nature Biotechnology, 35(4), 316-319. doi: 10.1038/nbt.3820
        • Ewels, P. A., Peltzer, A., Fillinger, S., Patel, H., Alneberg, J., Wilm, A., Garcia, M. U., Di Tommaso, P., & Nahnsen, S. (2020). The nf-core framework for community-curated bioinformatics pipelines. Nature Biotechnology, 38(3), 276-278. doi: 10.1038/s41587-020-0439-x
        • Grüning, B., Dale, R., Sjödin, A., Chapman, B. A., Rowe, J., Tomkins-Tinch, C. H., Valieris, R., Köster, J., & Bioconda Team. (2018). Bioconda: sustainable and comprehensive software distribution for the life sciences. Nature Methods, 15(7), 475–476. doi: 10.1038/s41592-018-0046-7
        • da Veiga Leprevost, F., Grüning, B. A., Alves Aflitos, S., Röst, H. L., Uszkoreit, J., Barsnes, H., Vaudel, M., Moreno, P., Gatto, L., Weber, J., Bai, M., Jimenez, R. C., Sachsenberg, T., Pfeuffer, J., Vera Alvarez, R., Griss, J., Nesvizhskii, A. I., & Perez-Riverol, Y. (2017). BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics (Oxford, England), 33(16), 2580–2582. doi: 10.1093/bioinformatics/btx192
        Notes:
        • The command above does not include parameters contained in any configs or profiles that may have been used. Ensure the config file is also uploaded with your publication!
        • You should also cite all software used within this run. Check the "Software Versions" of this report to get version information.

        nf-core/scrnaseq Workflow Summary

        - this information is collected when the pipeline is started.URL: https://github.com/nf-core/scrnaseq

        Input/output options

        input
        ../VU40Tsamplesheet.csv
        outdir
        ../LPS_VU40T_QC_and_counts

        Mandatory arguments

        aligner
        cellranger

        Reference genome options

        fasta
        ../../Refs/hg38.fa
        genome
        GRCh38
        gtf
        ../../Refs/hg38.ncbiRefSeq.gtf
        save_align_intermeds
        true

        Institutional config options

        config_profile_contact
        Alex Lyttle (@alexlyttle)
        config_profile_description
        BlueBEAR cluster profile
        config_profile_url
        https://docs.bear.bham.ac.uk/

        Generic options

        trace_report_suffix
        2025-05-04_19-22-55

        Core Nextflow options

        configFiles
        /rds/homes/d/daviesjp/.nextflow/assets/nf-core/scrnaseq/nextflow.config
        containerEngine
        singularity
        launchDir
        /rds/projects/g/gendood-3dmucosa/BaseSpace/scripts
        profile
        bluebear
        projectDir
        /rds/homes/d/daviesjp/.nextflow/assets/nf-core/scrnaseq
        revision
        4.0.0
        runName
        irreverent_euclid
        userName
        daviesjp
        workDir
        /rds/projects/g/gendood-3dmucosa/BaseSpace/scripts/work